Publications


Below my complete publication list. You may also check Google-Scholar, DBLP, ACM.

Journals

[1]   Trani, S., Losada, D. E., Lucchese, C., Perego, R., Ceccarelli, D., and Orlando, S. Sel: a unified algorithm for entity linking and saliency detection. Wiley Computational Intelligence (to appear).

[2]   Coletto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Renso, C. Perception of social phenomena through the multidimensional analysis of online social networks. Elsevier Online Social Networks and Media 1 (2017), 14 – 32. doi.

[3]   Coletto, M., Aiello, L. M., Lucchese, C., and Silvestri, F. Adult content consumption in online social networks. Springer Social Network Analysis and Mining 7, 1 (2017), 28. doi.

[4]   Lulli, A., Carlini, E., Dazzi, P., Lucchese, C., and Ricci, L. Fast connected components computation in large graphs by vertex pruning. IEEE Transactions on Parallel and Distributed Systems PP, 99 (2016). doi.

[5]   Dato, D., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Fast ranking with additive ensembles of oblivious and non-oblivious regression trees. ACM Transactions on Information Systems 35, 2 (2016), 15:1–15:31. doi.

[6]   Capannini, G., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. Quality versus efficiency in document scoring with learning-to-rank models. Information Processing & Management (2016). doi.

[7]   Lucchese, C., Orlando, S., and Perego, R. A unifying framework for mining approximate top-k binary patterns. IEEE Transactions On Knowledge and Data Engineering 26, 12 (2014), 2900–2913. IF. 2.067. doi.

[8]   Freris, N. M., Lucchese, C., Vlachos, M., and Zoumpoulis, S. Right-protected data publishing with provable distance-based mining. IEEE Transactions On Knowledge and Data Engineering 26, 8 (2014), 2014–2028. IF. 2.067. doi.

[9]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Discovering tasks from search engine query logs. ACM Trans. Inf. Syst. 31, 3 (2013), 14. (ACM Notable Article), IF. 1.300. doi.

[10]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Similarity caching in large-scale image retrieval. Information Processing & Management (Special Issue on Large-Scale and Distributed Systems for Information Retrieval) 48, 5 (2012), 803–818. IF. 0.817. doi.

[11]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Rights protection of trajectory datasets with nearest-neighbor preservation. VLDB Journal 19, 4 (2010), 531–556. IF. 2.198. doi.

[12]   Lucchese, C., Mastroianni, C., Orlando, S., and Talia, D. Mining@home: Towards a public resource computing framework for distributed data mining. Concurrency and Computation: Practice and Experience 22, 5 (2010), 658–682. IF. 0.907. doi.

[13]   Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., and Zezula, P. Building a web-scale image similarity search system. Multimedia Tools and Applications 47, 3 (2010), 599–629. IF. 0.885. doi.

[14]   Kozat, S. S., Vlachos, M., Lucchese, C., Herle, H. V., and Yu, P. S. Embedding and retrieving private metadata in electrocardiograms. Journal of Medical Systems 33, 4 (2009), 241–259. IF. 0.654. doi.

[15]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. A constraint-based querying system for exploratory pattern discovery. Information Systems 34, 1 (2009), 3–27. IF. 1.966. doi.

[16]   Bonchi, F., and Lucchese, C. Extending the state-of-the-art of constraint-based pattern discovery. Data and Knowledge Engineering 60, 2 (2007), 377–399. IF. 1.144. doi.

[17]   Lucchese, C., Orlando, S., and Perego, R. Fast and memory efficient mining of frequent closed itemsets. IEEE Transactions On Knowledge and Data Engineering 18, 1 (2006), 21–36. IF. 2.063. doi.

[18]   Bonchi, F., and Lucchese, C. On condensed representations of constrained frequent patterns. Knowledge and Information Systems 9, 2 (2006), 180–201. IF. 0.833. doi.

Conferences

[19]   Ruback, L., Casanova, M. A., Lucchese, C., and Renso, C. SELEcTor: Discovering similar entities on linked data by ranking their features. In ICSC ’17: IEEE International Conference on Semantic Computing (2017). (acceptance 20%).

[20]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Trani, S. X-dart: Blending dropouts and pruning for efficient learning to rank. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Short), (acceptance 30%).

[21]   Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Trani, S. Rankeval: An evaluation and analysis framework for learning-to-rank solutions. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Demo), (acceptance 47%).

[22]   Ferro, N., Lucchese, C., Maistro, M., and Perego, R. On including the user dynamic in learning to rank. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Short), (acceptance 30%).

[23]   Coletto, M., Garimella, K., Gionis, A., and Lucchese, C. A motif-based approach for identifying controversy. In ICWSM ’17: International AAAI Conference on Web and Social Media (2017). (Short).

[24]   Zneika, M., Lucchese, C., Vodislav, D., and Kotzinos, D. Summarizing linked data rdf graphs using approximate graph pattern mining. In EDBT ’16: Proceedings of the 19th International Conference on Extending Database Technology (2016). (poster).

[25]   Lucchese, C., Orlando, S., and Perego, R. Evaluating top-k approximate patterns via text clustering. In DAWAK ’16: 18th International Conference on Data Warehousing and Knowledge Discovery (2016).

[26]   Lucchese, C., Nardini, F. M., Orlando, S., and Tolomei, G. Learning to rank user queries to detect search tasks. In ICTIR ’16: International Conference on the Theory of Information Retrieval (2016).

[27]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Exploiting cpu simd extensions to speed-up document scoring with tree ensembles. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[28]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Silvestri, F., and Trani, S. Post-learning optimization of tree ensembles for efficient ranking. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[29]   Gigli, A., Lucchese, C., Nardini, F. M., and Perego, R. Fast feature selection for learning to rank. In ICTIR ’16: International Conference on the Theory of Information Retrieval (2016). (Short).

[30]   Coletto, M., Lucchese, C., Orlando, S., and Perego, R. Polarized user and topic tracking in twitter. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[31]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. SEL: a unified algorithm for entity linking and saliency detection. In DocEng ’16: Proceedings of the 2015 ACM Symposium on Document Engineering (2016). ((best student paper)).

[32]   Aiello, L. M., Coletto, M., Lucchese, C., and Silvestri, F. On the behaviour of deviant communities in online social networks. In ICWSM ’16: International AAAI Conference on Web and Social Media (2016).

[33]   Lucchese, C., Orlando, S., and Perego, R. Supervised evaluation of top-k itemset mining algorithms. In DAWAK ’15: 17th International Conference on Data Warehousing and Knowledge Discovery (2015).

[34]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Quickscorer: a fast algorithm to rank documents with additive ensembles of regression trees. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015). (best paper) (ACM Notable Article).

[35]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. Speeding up document ranking with rank-based features. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015). (Short).

[36]   Coletto, M., Lucchese, C., Orlando, S., Perego, R., Chessa, A., and Puliga, M. Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study. In IC2S2 ’15: Proceedings of the International Conference on Computational Social Science (2015). (poster).

[37]   Carlini, E., Dazzi, P., Lulli, A., Lucchese, C., and Ricci, L. Cracker: Crumbling large graphs into connected components. In ISCC ’15: Proceedings of the 20th IEEE Symposium on Computers and Communications (2015).

[38]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Manual annotation of semi-structured documents for entity-linking. In CIKM ’14: Proceedings of the 23rd ACM Intl. Conference on Information and Knowledge Management (2014), pp. 2075–2077. (Demo).

[39]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Dexter 2.0 – an open source tool for semantically enriching data. In ISWC ’14: Proceedings of the 13th Intl. Semantic Web Conference (2014), pp. 417–420. (Demo).

[40]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Modeling and predicting the task-by-task behavior of search engine users. In OAIR ’13: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (2013), pp. 77–84.

[41]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Learning relatedness measures for entity linking. In CIKM ’13: Proceedings of the 22st ACM international conference on Information and knowledge management (2013). (acceptance 16.9%). doi.

[42]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Perego, R. When entities meet query recommender systems: semantic search shortcuts. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (2013), pp. 933–938.

[43]   Morales, G. D. F., Gionis, A., and Lucchese, C. From chatter to headlines: Harnessing the real-time web for personalized news recommendation. In WSDM ’12: ACM International Conference on Web Search and Data Mining (2012). (acceptance 20.7%). doi.

[44]   Lucchese, C., Perego, R., Silvestri, F., Vahabi, H., and Venturini, R. How random walks can help tourism. In ECIR ’12: Proceedings of the 34th European conference on Advances in Information Retrieval (2012), pp. 195–206. (Short), (acceptance 21%). doi.

[45]   Blanco, R., Ceccarelli, D., Lucchese, C., Perego, R., and Silvestri, F. You should read this! let me explain you why: explaining news recommendations to users. In CIKM ’12: Proceedings of the 21st ACM international conference on Information and knowledge management (2012), pp. 1995–1999. (Short), (acceptance 27.8%). doi.

[46]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Identifying task-based sessions in search engine query logs. In WSDM ’11: ACM International Conference on Web Search and Data Mining (February 2011). (best 6), (acceptance 22%). doi.

[47]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Caching query-biased snippets for efficient retrieval. In EDBT ’11: ACM 14th International Conference on Extending Database Technology (2011), pp. 93–104.

[48]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Tolomei, G. Improving europeana search experience using query logs. In TPDL ’11: International Conference on Theory and Practice of Digital Libraries (Septemebr 2011).

[49]   Boley, M., Lucchese, C., Paurat, D., and Grtner, T. Direct local pattern sampling by efficient two-step random procedures. In KDD ’11: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August 21-24 2011), pp. 582–590. (VQR: 0.7), (acceptance 7.8%). doi.

[50]   Lucchese, C., Orlando, S., and Perego, R. Mining top-k patterns from binary datasets in presence of noise. In SDM ’10: Proceedings of the 2010 SIAM International Conference on Data Mining (April 2010). (best 12), (VQR: 1), (acceptance 24%). doi.

[51]   Lucchese, C., Orlando, S., and Perego, R. A generative pattern model for mining binary datasets. In SAC ’10: Proceedings of the 25th ACM Symposium on Applied Computing (March 2010). (poster), (acceptance 33%). doi.

[52]   Baraglia, R., Lucchese, C., and De Francisci Morales, G. Document similarity self-join with mapreduce. In ICDM ’10: Proceedings of the tenth IEEE International Conference on Data Mining (December 2010). (Short), (acceptance 19%). doi.

[53]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Caching content-based queries for robust and efficient image retrieval. In EDBT ’09: Proceedings of the twelfth International Conference on Extending Database Technology (March 2009), pp. 780–790. (VQR: 0.8), (acceptance 32%). doi.

[54]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Rights protection of multidimensional time-series datasets with neighborhood preservation. In ICDE ’08: Proceedings of the 2008 IEEE International Conference on Data Engineering (2008), pp. 1349–1351. (poster), (acceptance 31%). doi.

[55]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Ownership protection of shapes with geodesic distance preservation. In EDBT ’08: Proceedings of the eleventh International Conference on Extending Database Technology (2008), pp. 276–286. (VQR: 0.8), (acceptance 17%). doi.

[56]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. A metric cache for similarity search. In Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval (2008), ACM, pp. 43–50.

[57]   Baraglia, R., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. (Query) History teaches everything, including the future. In LA-WEB ’08: Proceedings of the sixth Latin American Web Congress (Invited) (2008), pp. 12–22. (invited). doi.

[58]   Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Mining query logs to optimize index partitioning in parallel web search engines. In INFOSCALE ’07: Proceedings of the Second International Conference on Scalable Information Systems (June 2007). (acceptance 16%). doi.

[59]   Lucchese, C., Orlando, S., and Perego, R. Parallel mining of frequent closed patterns: Harnessing modern computer architectures. In ICDM ’07: Proceedings of the Seventh IEEE International Conference on Data Mining (November 2007), pp. 242–251. (VQR: 1), (acceptance 7%). doi.

[60]   Lucchese, C., Orlando, S., and Perego, R. Mining frequent closed itemsets out of core. In SDM ’06: Proceedings of the third SIAM International Conference on Data Mining (April 2006). (acceptance 16%). doi.

[61]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. Conquest: a constraint-based querying system for exploratory pattern discovery. In ICDE ’06: Proceedings of the 2006 IEEE International Conference on Data Engineering (Demo) (2006), pp. 159–160.

[62]   Baraglia, R., Lucchese, C., Orlando, S., Serran, M., and Silvestri, F. A privacy preserving web recommender system. In SAC ’06: Proceedings of the 21st ACM Symposium on Applied Computing (April 2006), pp. 559–563. (acceptance 32%). doi.

[63]   Bonchi, F., and Lucchese, C. Pushing tougher constraints in frequent pattern mining. In PAKDD ’05: Proceedings of the Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (Hanoi, Vietnam, May 2005), pp. 114–124. (acceptance 15%). doi.

[64]   Bonchi, F., and Lucchese, C. On closed constrained frequent pattern mining. In ICDM ’04: Proceedings of the Fourth IEEE International Conference on Data Mining (November 2004), pp. 35–42. (acceptance 9%). doi.

Workshops, Chapters in Books and other publications

[65]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Speeding-up document scoring with tree ensembles using cpu simd extensions. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[66]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Ranking documents efficiently with quickscorer. In SEBD ’16: Proceedings of the 24th Italian Symposium on Advanced Database Systems (June 2016).

[67]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Silvestri, F., and Trani, S. Post-learning optimization of tree ensembles. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[68]   Lettich, F., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Gpu-based parallelization of quickscorer to speed-up document ranking with tree ensembles. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[69]   Coletto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Renso, C. Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. SNAST ’16: Workshop on Social Network Analysis Surveillance Technologies, colocated with ASONAM ’16: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2016).

[70]   Zneika, M., Lucchese, C., Vodislav, D., and Kotzinos, D. Rdf graph summarization based on approximate patterns. In ISIP ’15: PostProceeding of the 9th International Workshop on Information Search, Integration and Personalization (2015), Springer CCIS Series.

[71]   Coletto, M., Lucchese, C., Orlando, S., Perego, R., Chessa, A., and Puliga, M. Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study. ISTI 2015-TR-009: Poster accepted at ICCSS 2015 (2015).

[72]   Coletto, M., Lucchese, C., Orlando, S., and Perego, R. Electoral predictions with twitter: a machine-learning approach. IIR ’15: 6th Italian Information Retrieval Workshop (2015).

[73]   Capannini, G., Dato, D., Lucchese, C., Mori, M., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. QuickRank: a C++ suite of learning to rank algorithms. IIR ’15: 6th Italian Information Retrieval Workshop (2015).

[74]   Lucchese, C., Muntean, C. I., Perego, R., Silvestri, F., Vahabi, H., and Venturini, R. Recommender systems. Mining User Generated Content – Social Media and Social Computing (2014), 287–317.

[75]   Lucchese, C., Perego, R., Trani, S., Atzemoglou, M., Baurens, B., and Kotzinos, D. Ingeoclouds: A cloud-based platform for sharing geodata across europe. ERCIM News 2013, 94 (2013).

[76]   Lagarde, P., and Lucchese, C. From a classical web mapping publication to a inspire service architecture in the cloud. INSPIRE Conference (2013).

[77]   Ceccarelli, D., Lucchese, C., Orlando, S., and Tolomei, G. Twitter anticipates bursts of requests for wikipedia articles. In Workshop on Data-driven User Behavioral Modelling and Mining from Social Media (2013).

[78]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Dexter: an open source framework for entity linking. In Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR) (2013).

[79]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Perego, R. On suggesting entities as web search queries. In Proceedings of the 4th Italian Information Retrieval Workshop (2013), pp. 37–40.

[80]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., Perego, R., and Tolomei, G. Discovering europeana users’ search behavior. ERCIM News 2011, 86 (2011).

[81]   Boley, M., Lucchese, C., Paurat, D., and Gartner, T. Direct pattern sampling with respect to pattern frequency. KDML ’11: Workshop on Knowledge Discovery, Data Mining and Machine Learning, in conjunction with the LWA 2011 (September 2011).

[82]   Boley, M., Lucchese, C., Paurat, D., and Grtner, T. Direct pattern sampling with respect to pattern frequency. In KDML ’11: Workshop on Knowledge Discovery, Data Mining and Machine Learning (September 2011).

[83]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Detecting task-based query sessions using collaborative knowledge. In IWI ’10: International Workshop on Intelligent Web Interaction (August 2010).

[84]   Cambazoglu, B. B., and Lucchese, C. LSDS-IR ’11: 9th workshop on large-scale distributed systems for information retrieval. CIKM ’11: the 20th ACM Conference on Information and Knowledge Management 44, 2 (2010), 54–58.

[85]   Blanco, R., Cambazoglu, B. B., and Lucchese, C. LSDS-IR ’10: 8th workshop on large-scale distributed systems for information retrieval. SIGIR Forum 44, 2 (2010), 54–58.

[86]   Baraglia, R., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Preserving privacy in web recommender systems. In Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques. CRC, Taylor and Francis Group, LLC., 2010.

[87]   Baraglia, R., Lucchese, C., and De Francisci Morales, G. Scaling out all pairs similarity search with mapreduce. In LSDS-IR ’10: Proceedings of the eighth Workshop on Large-Scale Distributed Systems for Information Retrieval (July 2010).

[88]   Lucchese, C., Skobeltsyn, G., and Yee, W. G. LSDS-IR ’09: 7th workshop on large-scale distributed systems for information retrieval. SIGIR Forum 43, 2 (2009), 34–40. doi.

[89]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Caching algorithms for similarity search. In SEBD ’09: Proceedings of the Seventeenth Italian Symposium on Advanced Database Systems (June 2009).

[90]   Bolettieri, P., Falchi, F., Lucchese, C., Mass, Y., Perego, R., Rabitti, F., and Shmueli-Scheuer, M. Searching 100M images by content similarity. In IRCDL ’09: Post-proceedings of the 5th Italian Research Conference on Digital Library Systems, revised selected papers. (January 2009), DELOS: an Association for Digital Libraries, pp. 88–89.

[91]   Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., and Rabitti, F. Enabling content-based image retrieval in very large digital libraries. In Second Workshop on Very Large Digital Libraries (VLDL 2009), 2 October 2009, Corfu, Greece (Pisa, Italy, 2009), DELOS, pp. 43–50.

[92]   Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., and Rabitti, F. CoPhIR: a test collection for content-based image retrieval. CoRR abs/0905.4627v2 (2009).

[93]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. A metric cache for similarity search. In LSDS-IR ’08: Proceedings of the sixth Workshop on Large-Scale Distributed Systems for Information Retrieval (October 2008). ((VQR: 0.5))).

[94]   Falchi, F., and Lucchese, C. Special track on Engineering Large-Scale Distributed Systems: editorial message. In SAC ’08: Proceedings of the 2008 ACM Symposium on Applied Computing (March 2008), vol. 1, ACM, pp. 453–454.

[95]   Buehrer, G., Coppola, M., and Lucchese, C. HPDM ’08: Workshop on high performance data mining. In ICDM ’08: Proceedings of the Ninth IEEE International Conference on Data Mining (2008), IEEE Computer Society, pp. xxvii–xxix.

[96]   Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubský, J., and Zezula, P. Crawling, indexing, and similarity searching images on the web. In SEBD ’08: Proceedings of the Sixteenth Italian Symposium on Advanced Database Systems (June 2008), pp. 382–389.

[97]   Barbalace, D., Lucchese, C., Mastroianni, C., Orlando, S., and Talia, D. Mining@home: Public resource computing for distributed data mining. In Proceeding of CoreGRID Symposium 2008 (August 2008).

[98]   Lucchese, C., Orlando, S., Perego, R., and Silvestri, C. Mining frequent closed itemsets from distributed repositories. In Knowledge and Data Managment in GRIDS. CoreGRID Series by Springer, 2007.

[99]   Laforenza, D., Lucchese, C., Orlando, S., Perego, R., Puppin, D., and Silvestri, F. On the value of query logs for modern information retrieval. In DART ’06: Distributed Agent-based Retrieval Tools, The Future of Search Engines’ Technologies. Polimetrica, 2006, pp. 123–147.

[100]   Bonchi, F., Lucchese, C., Giannotti, F., Orlando, S., Perego, R., and Trasarti, R. On interactive pattern mining from relational databases. In SEBD ’06: Proceedings of the Fourteenth Italian Symposium on Advanced Database Systems (June 2006), pp. 329–338.

[101]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. On interactive pattern mining from relational databases. In KDID ’06: Knowledge Discovery in Inductive Databases, Revised Selected and Invited Papers. Lecture Notes in Computer Science by Springer, 2006.

[102]   Lucchese, C., Orlando, S., and Perego, R. On distributed closed itemsets mining: some preliminary results. In HPDM ’05: Proceedings of the eight SIAM SDM 2004 Workshop on High Performace Distributed Data Mining (April 2005), pp. 562–567.

[103]   Lucchese, C., Orlando, S., and Perego, R. WebDocs: a real-life huge transactional datase. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[104]   Lucchese, C., Orlando, S., and Perego, R. kDCI: on using direct count up to the third iteration. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[105]   Lucchese, C., Orlando, S., and Perego, R. DCI_Closed: A fast and memory efficient algorithm to mine frequent closed itemsets. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[106]   Lucchese, C., Orlando, S., Palmerini, P., Perego, R., and Silvestri, F. kDCI: a multi-strategy algorithm for mining frequent sets. In FIMI ’03: Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (November 2003).

Patents

[107]   Dato, D., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. A method to rank documents by a computer, using additive ensembles of regression trees and cache optimization, and search engine using such a method. Tiscali S.p.A. PCT29914, (Application) (2015).

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